With the rapid rise in data availability over the past decade, the amount of data processed by modern businesses has far surpassed the resources offered by traditional databases. With the integration of artificial intelligence (AI) and machine learning, complex data parsing and analysis are now common functions of modern businesses. However, the frequency at which new data is made available makes it difficult to process and store in an affordable and manageable way.1
Data warehouses and third-party solutions such as Amazon Web Services have made progress in addressing this problem, but they attract high costs and limited accessibility. To meet the demand for low-cost, easily accessible, highly scalable database requirements, data lakes have risen in popularity. Open-source Apache Hadoop software is one such data lake solution, providing extremely high scalability at a low cost.2
What is Hadoop?
Those new to database technology may not be familiar with Hadoop, but it has been around for almost two decades. In 2002, before Google rose to prominence, Doug Cutting and Mike Cafarella developed a distributed storage and search engine called Nutch. After taking a position at Yahoo in 2006, Cutting divided the Nutch platform into two separate parts, labeling the distributed storage section as Hadoop.
Soon after, Yahoo released Hadoop as an open-source project and it eventually fell under the supervision of the non-profit Apache Software Foundation (ASF). As a free-to-use, open-source project, Hadoop has been highly beneficial in progressing modern database-related technologies like the Internet-of-Things (IoT), Big Data and AI. It's typically used to store a range of nonrelational data such as internet records, log files, images and sensor metrics.3
Rather than focusing on rapid processing speed, Hadoop is geared toward the storage of massive amounts of data from multiple sources. It's a highly scalable, distributed computing solution that can support thousands of servers in a single data lake. For fault tolerance, Hadoop addresses failures on the application layer, maintaining high availability without the need for immediate hardware replacement.4
What are data lakes?
As the name suggests, a data lake is populated by information from a wide range of sources in the same way streams feed into a real lake. Unlike the name suggests, a data lake is not necessarily a body of data stored in one large container; it is often widely dispersed over several servers. Its main advantages are low-cost storage and a more flexible data processing environment that supports multiple formats and file types.5
Although the data is distributed, the repository is centralized, providing a single point of access to large volumes of raw data. Unlike in a data warehouse, information in a data lake is often undefined and unstructured, commonly accessed by AI algorithms that can quickly extract and transform data without the need for strict organization.
This makes data lakes particularly useful in situations where large amounts of raw data are being collected in an automated fashion. Data lakes also serve as repositories for data that doesn't fit into the model of an organization's main data warehouse. In some situations, the data in a data lake can be stored for years before being used, if it is used at all. For data scientists, this environment provides a wealth of previously undiscovered data, such as metrics and statistics, that are ripe for analysis.6
The Pros and Cons of Hadoop Data Lakes
While Hadoop Data Lake has several powerful advantages over other database solutions, it isn't perfect. Depending on your business type and requirements, you'll need to assess whether the benefits outweigh the limitations.
The key advantages of the Hadoop Data Lake environment include:7
- Rapid storage and processing of massive amounts of data from multiple sources
- Extremely high fault tolerance as a result of distributed nodes with automated redirection and failover processing
- Ability to store data in any format without the need for preprocessing
- Free-to-use, open-source software that uses cheap commodity hardware for high-quantity storage
Despite these impressive credentials, Hadoop Data Lake still has some disadvantages:
- Hadoop uses MapReduce programming, which isn't ideal for iterative and interactive analytical tasks due to the high number of phases and files required
- MapReduce is not intuitive and has a high learning curve, attracting a very limited supply of skilled programmers
- Although improvements are being made, Hadoop's fragmented data environment lacks the high security of some other database solutions
- Hadoop is unsuitable for real-time data interaction, as it uses only batch processing
Hadoop's ability to store and process data from multiple sources gives it a significant advantage over other data lake solutions. However, this feature also makes it slightly slower and less suitable for processing data in real time.
Unlike some other data lake solutions, Hadoop uses batch processing rather than stream processing. This also makes it less suitable for real-time processing but ideal for processing large datasets.8
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Sources:
1. Retrieved on May 18, 2021 from researchgate.net/publication/264624667_The_rise_of_Big_Data_on_cloud_computing_Review_and_open_research_issues
2. Retrieved on May 18, 2021 from segment.com/blog/data-lakes/
3. Retrieved on May 18, 2021 from sas.com/en_us/insights/big-data/hadoop.html
4. Retrieved on May 18, 2021 from searchdatamanagement.techtarget.com/definition/Hadoop-data-lake
5. Retrieved on May 18,2021 from qlik.com/us/data-lake/data-lake-vs-data-warehouse
6. Retrieved on May 18, 2021 from bluegranite.com/blog/bid/402596/top-five-differences-between-data-lakes-and-data-warehouses
7. Retrieved on May 18, 2021 from data-flair.training/blogs/advantages-and-disadvantages-of-hadoop/
8. Retrieved on May 18, 2021 from geeksforgeeks.org/hadoop-pros-and-cons/